From Bloomberg via Energy Connects, March 7:
At midnight every day in Bologna, Italy, rows of supercomputers inside a former tobacco factory start churning through millions of measurements to predict how the Earth’s weather will change.
Six hours later, energy traders all over Europe rise and refresh their browsers to get the most updated outlook. Those mainframe-generated forecasts are often the biggest factor helping them make money by knowing where and when to move energy around the power grid — but a new model that runs on artificial intelligence is threatening to make them obsolete.
Unlike standard weather simulations, which only crunch information from satellites, sensors and the like, the AI model from Europe’s intergovernmental forecasting center also feasts on historical data. Before its release late last month, the center tested the new method against its conventional model produced in Bologna and found the AI more accurately predicted temperature, precipitation, wind and tropical cyclones, all with less computing energy.
The model is poised to help traders in Europe and around the world make quicker moves in power and natural gas markets convulsed by extreme weather, geopolitics and fluctuations in renewable sources. It’s a technology that could help minimize energy gluts and shortfalls in the world’s fastest-warming continent, as well as provide information key to deciding where wind and solar farms should be built.
“We can update our information set more often than we are used to” because of the European center’s AI model, said Daniel Borup, chief executive officer of Danish trading firm InCommodities A/S. “That obviously leads to improvements in our predictions. It allows us to improve our job and distribute energy better.”
Like its traditional outlook, the European Centre for Medium-Range Weather Forecasts’ new system — the first AI model released by a major prediction center — estimates temperatures, wind speeds and solar power two weeks in advance. But its improved accuracy means companies and policymakers can move faster on critical weather-related decisions, from canceling rail service to routing ships around storms and dispatching trucks to spread sand on icy roads, according to the center.
That degree of forecasting prowess will could prove essential to managing market volatility. Earlier this month, robust generation from solar parks in Germany pushed power prices in several countries below zero. That was a reversal from earlier in the year, when a stretch of cloudy and windless weather known as a Dunkelflaute curbed renewable output and sent German electricity prices soaring.
The upgrade is a radical shift away from the standard approach of using supercomputers to crunch millions of measurements to recreate a snapshot of the atmosphere’s physics, and then fast-forwarding the model to predict how the weather will change.
Climate and weather datasets were already structured perfectly for AI and could use machine learning techniques developed for other scientific research approaches, Florian Pappenberger, the European center’s deputy director-general and lead forecaster.
“Weather and climate is a Big Data problem,” he said. “We have huge amounts of data — humongous amounts — so it’s a perfect match” for the center’s new model, he added....
....MUCH MORE
As we said introducing December's ""Weather Derivatives Are Booming in an Unpredictable Climate"":
For when you're jonesin' for some complex/chaotic action but just can't seem to scratch that itch, superimpose one complex/chaotic system, markets, on top of another complex/chaotic system, weather, and away you go.
And in 2018:
"Machine Learning’s ‘Amazing’ Ability to Predict Chaos"
Researchers have used machine learning to predict the chaotic evolution of a model flame front.
When you have one complex-chaotic system, say an ag or energy derivatives market overlaid on another complex-chaotic system, say, for example, weather; the ability to foretell the progression from the initial condition of one, or better yet both, systems would have some pecuniary advantage****Hey, I've made that bet! It's called "The ol' just light large-denomination banknotes on fire to avoid the hassle of feigning any type of skill or expertise in weird instruments you don't understand trade."**
And 2016:
IBM's Watson Gets A Real Job: Big Blue Closes Purchase of The Weather Company
Complex-chaotic requires big horsepower to figure out. It'll probably take the quantum machines coming down the pike but this is a start and more worthy of Watson than Jeopardy!
And 2009
A milder hurricane season could still juice energy. And: Knowing your ENSO from a Hole in the Sea
I disagree with the premise of the first piece, it appears the author is practicing "pop hurricanology" but thought I should post it to show the practical [profitable? -ed] advantage of understanding this stuff. Right now, the expectations are for not just fewer hurricanes but for those that do form to have a slight tendency toward a more northerly track i.e. east coast vs. gulf coast landfalls.
With the caveat, of course, that these are some of the most complex systems that humans try to predict and being chaotic, it is probably a Fool's [trader's? -ed] game anyway....